Overview

Kimi K2.7 Code stands as a powerhouse within the Kimi-k2 family, engineered specifically for complex reasoning and high-fidelity code generation. With 1042 billion parameters quantized to INT4, this thinking model delivers exceptional performance across Arabic and English tasks, making it ideal for research pipelines requiring bilingual precision. The massive 262,144 token context window allows developers to process entire codebases or lengthy documentation in a single pass, ensuring comprehensive understanding without fragmentation. Benchmark data indicates superior accuracy compared to standard alternatives, validating its suitability for demanding AI applications.

Accessible via the LLM Resayil platform, this model is designed for seamless enterprise integration. While operating on an 8x credit multiplier relative to the base rate, it requires an enterprise tier subscription, guaranteeing production-ready stability and support. Developers can initiate their first API call within minutes using our straightforward documentation, bypassing complex setup procedures. We provide transparent billing options compatible with regional currencies, ensuring business decision makers have clear cost visibility without needing sales consultations. Our infrastructure ensures low-latency responses suitable for real-time applications. Whether building sophisticated agents or analyzing large datasets, Kimi K2.7 Code offers the reliability and scale necessary for mission-critical deployments.

Specifications

Display Name Kimi K2.7 Code

Family Kimi-k2

Category Vision

Parameters 1042B

Context Window 262,144 tokens

Quantization INT4

License OTHER

Min Tier Enterprise

Status Available

Pricing

8×

credits per token

1K 8,000 Credits

10K 80,000 Credits

100K 800,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="kimi-k2.7-code",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "kimi-k2.7-code",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "kimi-k2.7-code",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Refactoring large legacy codebases efficiently and safely

Creating detailed API documentation from source code

Debugging complex distributed system errors effectively

Analyzing entire repositories for security vulnerabilities

Migrating monolithic applications to microservices architecture

In-Depth Guide

Full Guide

Complete Guide to Kimi K2.7 Code — LLM Resayil

Kimi K2.7 Code